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Abstract:
We present a new method for the blind separation of sources,
which do not fulfill the independence assumption. In contrast to
standard methods we consider groups of neighboring samples
(``patches'') within the observed mixtures.
First we extract independent features from the observed
patches. It turns out that the average dependencies between these
features in different sources is in general lower than the
dependencies between the amplitudes of different sources. We show
that it might be the case that most of the dependencies is
carried by only a small number of features. Is this case --
provided these features can be identified by some heuristic -- we
project all patches into the subspace which is orthogonal to the
subspace spanned by the ``correlated'' features.
Standard ICA is then performed on the elements of the
transformed patches (for which the independence assumption holds)
and robustly yields a good estimate of the mixing matrix.
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